import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import matplotlib.pyplot as plt


def plot_history(history):
    """Plot Results of Keras training"""
    plt.style.use("fivethirtyeight")
    epochs = list(range(1, len(history["loss"]) + 1))
    plt.figure(figsize=(18, 6))

    # Losses
    plt.subplot(1, 2, 1)
    plt.plot(epochs, history["loss"], "-o", ms=10, label="Training Loss")
    plt.plot(epochs, history["val_loss"], "-*", ms=10, label="Validation Loss")
    plt.legend()
    plt.xlabel("Epoch")
    plt.ylabel("Loss")
    plt.title("Losses")

    # Accuracy
    plt.subplot(1, 2, 2)
    plt.plot(epochs, history["acc"], "-o", ms=10, label="Training Acc")
    plt.plot(epochs, history["val_acc"], "-*", ms=10, label="Validation Acc")
    plt.legend()
    plt.xlabel("Epoch")
    plt.ylabel("Acc")
    plt.title("Accuracy")

    plt.suptitle("Training Curves", y=1.05)


def get_options(slack):
    command_dict = {"functions": {}, "attributes": {}}

    # Modules
    for d in dir(slack):
        if not d.startswith("_"):
            command_dict["functions"][d] = []
            command_dict["attributes"][d] = []
            # Iterate through methods and attributes
            for dd in dir(getattr(slack, d)):
                if not dd.startswith("_"):
                    # List of methods and attributes
                    l = dir(getattr(getattr(slack, d), dd))
                    # Method (function)
                    if "__call__" in l:
                        command_dict["functions"][d].append(dd)
                    # Attributes
                    else:
                        command_dict["attributes"][d].append(dd)

    return command_dict


def get_data_and_model():
    batch_size = 128
    num_classes = 10
    epochs = 12

    # input image dimensions
    img_rows, img_cols = 28, 28

    # the data, split between train and test sets
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    if K.image_data_format() == "channels_first":
        x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
        x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
        input_shape = (1, img_rows, img_cols)
    else:
        x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
        x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
        input_shape = (img_rows, img_cols, 1)

    x_train = x_train.astype("float32")
    x_test = x_test.astype("float32")
    x_train /= 255
    x_test /= 255
    print("x_train shape:", x_train.shape)
    print(x_train.shape[0], "train samples")
    print(x_test.shape[0], "test samples")

    # convert class vectors to binary class matrices
    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)

    model = Sequential()
    model.add(
        Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=input_shape)
    )
    model.add(Conv2D(64, (3, 3), activation="relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation="relu"))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation="softmax"))

    model.compile(
        loss=keras.losses.categorical_crossentropy,
        optimizer=keras.optimizers.Adadelta(),
        metrics=["accuracy"],
    )

    return x_train, x_test, y_train, y_test, model
